Detecting and responding to processions for autonomous vehicles

ABSTRACT

The technology relates to detecting and responding to processions. For instance, sensor data identifying two or more objects in an environment of a vehicle may be received. The two or more objects may be determined to be disobeying a predetermined rule in a same way. Based on the determination that the two or more objects are disobeying a predetermined rule, that the two or more objects are involved in a procession may be determined. The vehicle may then be controlled autonomously in order to respond to the procession based on the determination that the two or more objects are involved in a procession.

CROSS REFERENCE TO RELATED APPLICATIONS

The present application is a continuation of U.S. patent applicationSer. No. 16/105,233, filed Aug. 20, 2018, the disclosure of which ishereby incorporated herein by reference.

BACKGROUND

Autonomous vehicles, such as vehicles that do not require a humandriver, can be used to aid in the transport of passengers or items fromone location to another. Such vehicles may operate in a fully autonomousmode where passengers may provide some initial input, such as a pickupor destination location, and the vehicle maneuvers itself to thatlocation.

Robust operation of an autonomous vehicle or a vehicle operating in anautonomous driving mode requires proper response to unexpectedcircumstances, such as when a vehicle encounters a procession.Processions may include groups of vehicles or persons moving together,such as in a parade, march, funeral procession, motorcade, etc. Whilethese are rare situations, they typically invoke completely differenttraffic rules, either legally or in terms of courtesy. Therefore beingable to detect and respond to such processions can be especiallyimportant to ensuring a safe and effective autonomous driving.

BRIEF SUMMARY

Aspects of the disclosure provide a method of detecting and respondingto processions. The method includes receiving, by one or moreprocessors, sensor data identifying two or more objects in anenvironment of a vehicle; determining, by the one or more processors,that the two or more objects are disobeying a predetermined rule in asame way; based on the determination that the two or more objects aredisobeying a predetermined rule, determining, by the one or moreprocessors, that the two or more objects are involved in a procession;and controlling, by the one or more processors, the vehicle autonomouslyin order to respond to the procession based on the determination thatthe two or more objects are involved in a procession.

In one example, the predetermined rule defines a traffic precedence thatthe two or more objects are disobeying. In another example, determiningthat the two or more objects are involved in a procession is based on athreshold minimum number of objects determined to be disobeying thepredetermined rule. In this example, determining that the two or moreobjects are involved in a procession is based on the threshold minimumnumber of objects disobeying the predetermined rule over at least athreshold minimum period of time. In another example, determining thatthe two or more objects are involved in a procession is based on objectsdetermined to be disobeying the predetermined rule over at least athreshold minimum period of time. In another example, the method alsoincludes determining that the procession is a motorcade or a funeralprocession. In another example, the method also includes determiningthat the procession is a parade or march. In another example, thepredetermined rule includes one of stopping for a red traffic lightsignal or stopping for a stop sign. In another example, determining, bythe one or more processors, that the two or more objects are involved inthe procession is further based on a number of additional objects eachdisobeying the predetermined rule. In another example, determining, bythe one or more processors, that the two or more objects are involved inthe procession is further based on an amount of time during whichadditional objects also disobey the predetermined rule. In anotherexample, the method also includes identifying an emergency vehicle fromthe sensor data, and wherein determining that the two or more objectsare involved in the procession is further based on the emergencyvehicle. In this example, determining that the two or more objects areinvolved in the procession is further based on a location of theemergency vehicle relative to the two or more objects. In this example,determining that the two or more objects are involved in the processionis further based on whether the emergency vehicle is stopped in anintersection. In another example, the method also includes identifying apedestrian directing traffic from the sensor data, and whereindetermining that the two or more objects are involved in the processionis further based on the identified pedestrian directing traffic.

In another example, the method also includes identifying a pedestrianholding a flag, a sign, or a banner from the sensor data, and whereindetermining that the two or more objects are involved in the processionis further based on the identified pedestrian holding the flag, the signor the banner. In another example, the method also includes identifyingthat at least one of the two or more objects has a flag or badge, andwherein determining that the two or more objects are involved in theprocession is further based on the identification that the at least oneof the two or more objects has a flag or badge. In another example, themethod also includes determining that an additional object between twoof the two or more objects is not disobeying the predetermined rule, andwherein determining that the two or more objects are involved in theprocession is further based on the determination that the additionalobject between two of the two or more objects is not disobeying thepredetermined rule. In another example, the method also includesdetermining a gap in time between two of the two or more objectsdisobeying the predetermined rule in the same way, and whereindetermining that the two or more objects are involved in the processionis further based on the gap in time. In another example, the method alsoincludes determining that the two or more objects are involved in theprocession is further based on local regulations for processions. Inanother example, the method also includes controlling the vehicleincludes yielding to the two or more objects involved in the processionas a group.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 is a functional diagram of an example vehicle in accordance withaspects of the disclosure.

FIG. 2 is an example representation of detailed map information inaccordance with aspects of the disclosure.

FIG. 3 is an example external view of a vehicle in accordance withaspects of the disclosure.

FIG. 4 is a pictorial diagram of an example system in accordance withaspects of the disclosure.

FIG. 5 is a functional diagram of the system of FIG. 4 in accordancewith aspects of the disclosure.

FIG. 6 is a view of a section of roadway in accordance with aspects ofthe disclosure.

FIG. 7 is a view of a section of roadway and sensor data in accordancewith aspects of the disclosure.

FIG. 8 is another view of a section of roadway and sensor data inaccordance with aspects of the disclosure.

FIG. 9 is a flow diagram in accordance with aspects of the disclosure.

DETAILED DESCRIPTION

Overview

The technology relates to detecting and responding to processions forfully autonomous vehicles or vehicles operating in an autonomous drivingmode. Processions may include groups of vehicles or persons movingtogether, such as in a parade, march, funeral procession, motorcade,etc. While these are rare situations, they typically invoke completelydifferent traffic rules, either legally or in terms of courtesy.Therefore being able to detect and respond to such processions can beespecially important to ensuring a safe and effective autonomousdriving.

The vehicle's perception system may use various sensors to detect andidentify objects in the vehicle's surroundings. This information may beused to determine how to maneuver the vehicle based on a combination ofthis information, map data, as well as rules for responding to differentsituations.

In order to detect a procession, the sensor data from the perceptionsystem may be analyzed to determine whether any detected objects aredisobeying one of the rules. Using this information, a processiondetector may be used to detect whether the object is involved in aprocession. In one instance, the detector may determine whether there isa procession based on whether one or more thresholds is met. In otherinstances, whether or not objects are involved in a procession may bemore nuanced. For instance, each additional object are disobeying thesame rule in the same way may increase a likelihood that the objects areinvolved in a procession. In addition or alternatively, for eachadditional second or more or less of time that objects continue todisobey the same rule in the same way may also increase a likelihoodthat the objects are involved in a procession.

Other signals may further increase or decrease the likelihood of aprocession. For instance, visual signals, such as identifying an objectmay also increase the likelihood of a procession. If there are objectswhich do not appear to be disobeying the same rule in the same way, thismay decrease or have no effect on the likelihood of a procession.Similarly, if there is a gap in time during which objects are obeyingthe rule, this may decrease the likelihood of a procession. In someinstances, local regulations may be taken into account by the detector.

Once the likelihood meets a certain threshold, the detector maydetermine that there is a procession. Once the detector has determinedthat there is a procession, this information may be sent to thevehicle's planner and used in order to determine how to respond to theprocession and therefore how to control the vehicle.

The features described herein may provide for a useful way to detect andrespond to procession. By doing so, the vehicle can respond to theprocession, for instance yielding appropriately. This may keep thevehicle becoming aggressive or assertive inappropriately as well aspreventing an uncomfortable stoppage of traffic by not appropriatelyresponding to processions.

Example Systems

As shown in FIG. 1 , a vehicle 100 in accordance with one aspect of thedisclosure includes various components. While certain aspects of thedisclosure are particularly useful in connection with specific types ofvehicles, the vehicle may be any type of vehicle including, but notlimited to, cars, trucks, motorcycles, busses, recreational vehicles,etc. The vehicle may have one or more computing devices, such ascomputing devices 110 containing one or more processors 120, memory 130and other components typically present in general purpose computingdevices.

The memory 130 stores information accessible by the one or moreprocessors 120, including instructions 132 and data 134 that may beexecuted or otherwise used by the processor 120. The memory 130 may beof any type capable of storing information accessible by the processor,including a computing devices-readable medium, or other medium thatstores data that may be read with the aid of an electronic device, suchas a hard-drive, memory card, ROM, RAM, DVD or other optical disks, aswell as other write-capable and read-only memories. Systems and methodsmay include different combinations of the foregoing, whereby differentportions of the instructions and data are stored on different types ofmedia.

The instructions 132 may be any set of instructions to be executeddirectly (such as machine code) or indirectly (such as scripts) by theprocessor. For example, the instructions may be stored as computingdevices code on the computing devices-readable medium. In that regard,the terms “instructions” and “programs” may be used interchangeablyherein. The instructions may be stored in object code format for directprocessing by the processor, or in any other computing devices languageincluding scripts or collections of independent source code modules thatare interpreted on demand or compiled in advance. Functions, methods androutines of the instructions are explained in more detail below.

The data 134 may be retrieved, stored or modified by processor 120 inaccordance with the instructions 132. The one or more processor 120 maybe any conventional processors, such as commercially available CPUs.Alternatively, the one or more processors may be a dedicated device suchas an ASIC or other hardware-based processor. Although FIG. 1functionally illustrates the processor, memory, and other elements ofcomputing devices 110 as being within the same block, it will beunderstood by those of ordinary skill in the art that the processor,computing devices, or memory may actually include multiple processors,computing devices, or memories that may or may not be stored within thesame physical housing. As an example, internal electronic display 152may be controlled by a dedicated computing devices having its ownprocessor or central processing unit (CPU), memory, etc. which mayinterface with the computing devices 110 via a high-bandwidth or othernetwork connection. In some examples, this computing devices may be auser interface computing devices which can communicate with a user'sclient device. Similarly, the memory may be a hard drive or otherstorage media located in a housing different from that of computingdevices 110. Accordingly, references to a processor or computing deviceswill be understood to include references to a collection of processorsor computing devices or memories that may or may not operate inparallel.

Computing devices 110 may all of the components normally used inconnection with a computing devices such as the processor and memorydescribed above as well as a user input 150 (e.g., a mouse, keyboard,touch screen and/or microphone) and various electronic displays (e.g., amonitor having a screen or any other electrical device that is operableto display information). In this example, the vehicle includes aninternal electronic display 152 as well as one or more speakers 154 toprovide information or audio visual experiences. In this regard,internal electronic display 152 may be located within a cabin of vehicle100 and may be used by computing devices 110 to provide information topassengers within the vehicle 100. In addition to internal speakers, theone or more speakers 154 may include external speakers that are arrangedat various locations on the vehicle in order to provide audiblenotifications to objects external to the vehicle 100.

In one example, computing devices 110 may be an autonomous drivingcomputing system incorporated into vehicle 100. The autonomous drivingcomputing system may capable of communicating with various components ofthe vehicle. For example, returning to FIG. 1 , computing devices 110may be in communication with various systems of vehicle 100, such asdeceleration system 160 (for controlling braking of the vehicle),acceleration system 162 (for controlling acceleration of the vehicle),steering system 164 (for controlling the orientation of the wheels anddirection of the vehicle), signaling system 166 (for controlling turnsignals), routing system 168 (for navigating the vehicle to a locationor around objects), positioning system 170 (for determining the positionof the vehicle), perception system 172 (for detecting objects in anexternal environment of the vehicle), and power system 174 (for example,a battery and/or gas or diesel powered engine) in order to control themovement, speed, etc. of vehicle 100 in accordance with the instructions132 of memory 130 in an autonomous driving mode which does not requireor need continuous or periodic input from a passenger of the vehicle.Again, although these systems are shown as external to computing devices110, in actuality, these systems may also be incorporated into computingdevices 110, again as an autonomous driving computing system forcontrolling vehicle 100.

The computing devices 110 may control the direction and speed of thevehicle by controlling various components. By way of example, computingdevices 110 may navigate the vehicle to a destination locationcompletely autonomously using data from the map information and routingsystem 168. Computing devices 110 may use the positioning system 170 todetermine the vehicle's location and perception system 172 to detect andrespond to objects when needed to reach the location safely. In order todo so, computing devices 110 may cause the vehicle to accelerate (e.g.,by increasing fuel or other energy provided to the engine byacceleration system 162), decelerate (e.g., by decreasing the fuelsupplied to the engine, changing gears, and/or by applying brakes bydeceleration system 160), change direction (e.g., by turning the frontor rear wheels of vehicle 100 by steering system 164), and signal suchchanges (e.g., by lighting turn signals of signaling system 166). Thus,the acceleration system 162 and deceleration system 160 may be a part ofa drivetrain that includes various components between an engine of thevehicle and the wheels of the vehicle. Again, by controlling thesesystems, computing devices 110 may also control the drivetrain of thevehicle in order to maneuver the vehicle autonomously.

As an example, computing devices 110 may interact with decelerationsystem 160 and acceleration system 162 in order to control the speed ofthe vehicle. Similarly, steering system 164 may be used by computingdevices 110 in order to control the direction of vehicle 100. Forexample, if vehicle 100 configured for use on a road, such as a car ortruck, the steering system may include components to control the angleof wheels to turn the vehicle. Signaling system 166 may be used bycomputing devices 110 in order to signal the vehicle's intent to otherdrivers or vehicles, for example, by lighting turn signals or brakelights when needed.

Routing system 168 may be used by computing devices 110 in order todetermine and follow a route to a location. In this regard, the routingsystem 168 and/or data 134 may store detailed map information, e.g.,highly detailed maps identifying the shape and elevation of roadways,lane lines, intersections, crosswalks, speed limits, traffic lightsignals, buildings, signs, real time traffic information, vegetation, orother such objects and information. In other words, this detailed mapinformation may define the geometry of vehicle's expected environmentincluding roadways as well as speed restrictions (legal speed limits)for those roadways

FIG. 2 is an example of map information 200 for a section of roadwayincluding intersections 202 and 204. In this example, the mapinformation 200 includes information identifying the shape, location,and other characteristics of lane lines 210, 212, 214, traffic lightsignal lights 220, 222, crosswalk 230, sidewalks 240, stop signs 250,252, and yield sign 260. Areas where the vehicle can drive may beassociated with one or more rails 270, 272, and 274 which indicate thelocation and direction in which a vehicle should generally travel atvarious locations in the map information. For example, a vehicle mayfollow rail 270 when driving in the lane between lane lines 210 and 212,and may transition to rail 272 in order to make a right turn atintersection 204. Thereafter the vehicle may follow rail 274. Of course,given the number and nature of the rails only a few are depicted in mapinformation 200 for simplicity and ease of understanding.

Although the detailed map information is depicted herein as animage-based map, the map information need not be entirely image based(for example, raster). For example, the detailed map information mayinclude one or more roadgraphs or graph networks of information such asroads, lanes, intersections, and the connections between these features.Each feature may be stored as graph data and may be associated withinformation such as a geographic location and whether or not it islinked to other related features, for example, a stop sign may be linkedto a road and an intersection, etc. In some examples, the associateddata may include grid-based indices of a roadgraph to allow forefficient lookup of certain roadgraph features.

The perception system 172 also includes one or more components fordetecting objects external to the vehicle such as other vehicles,obstacles in the roadway, traffic light signals, signs, trees, etc. Forexample, the perception system 172 may include one or more LIDARsensors, sonar devices, radar units, cameras and/or any other detectiondevices that record sensor data which may be processed by computingdevices 110. The sensors of the perception system may detect objects andtheir characteristics such as location, orientation, size, shape, type(for instance, vehicle, person or pedestrian, bicyclist, etc.), heading,and speed of movement, etc. The raw data from the sensors and/or theaforementioned characteristics can be quantified or arranged into adescriptive function, vector, and or bounding box and sent as sensordata for further processing to the computing devices 110 periodicallyand continuously as it is generated by the perception system 172. Asdiscussed in further detail below, computing devices 110 may use thepositioning system 170 to determine the vehicle's location andperception system 172 to detect and respond to objects when needed toreach the location safely.

For instance, FIG. 3 is an example external view of vehicle 100. In thisexample, roof-top housing 310 and dome housing 312 may include a LIDARsensor as well as various cameras and radar units. In addition, housing320 located at the front end of vehicle 100 and housings 330, 332 on thedriver's and passenger's sides of the vehicle may each store a LIDARsensor. For example, housing 330 is located in front of driver door 360.Vehicle 100 also includes housings 340, 342 for radar units and/orcameras also located on the roof of vehicle 100. Additional radar unitsand cameras (not shown) may be located at the front and rear ends ofvehicle 100 and/or on other positions along the roof or roof-top housing310.

Memory 130 may also store predetermined rules that the computing devices110 may use to determine how to respond to objects in the vehicle'senvironment and/or different situations. The rules may be configured asheuristics, decision trees, or other models but may define trafficprecedence in certain situations. For instance, if a pedestrian is in acrosswalk, a rule may require that the vehicle must yield to thepedestrian, or if a traffic light signal associated with a particularlane at an intersection is red and the vehicle is in that lane, thevehicle must stop before proceeding through the intersection. Theserules may be stored in the memory 130 and/or may be partiallyincorporated into the map information. For instance, each of stop signs250, 252 may be associated with one or more rules indicating the lane orlanes from which the stop sign is to be obeyed, that obeying the stopsign means stopping within a certain distance of some location (such asintersection 204), the precedence of road users (vehicles, bicyclists,motorcyclists, etc.) stopping at intersection 204, and where to stop thevehicle. As another example, each of the traffic light signal lights220, 222 may be associated with a rule indicating which lane iscontrolled by that traffic light signal light.

Based on these determinations, the computing devices 110 may control thedirection and speed of the vehicle by controlling various components. Byway of example, computing devices 110 may navigate the vehicle to adestination location completely autonomously using data from thedetailed map information and routing system 168. Computing devices 110may use the positioning system 170 to determine the vehicle's locationand perception system 172 to detect and respond to objects when neededto reach the location safely. In order to do so, computing devices 110may cause the vehicle to accelerate (e.g., by increasing fuel or otherenergy provided to the engine by acceleration system 162), decelerate(e.g., by decreasing the fuel supplied to the engine, changing gears,and/or by applying brakes by deceleration system 160), change direction(e.g., by turning the front or rear wheels of vehicle 100 by steeringsystem 164), and signal such changes (e.g., by lighting turn signals ofsignaling system 166). Thus, the acceleration system 162 anddeceleration system 160 may be a part of a drivetrain that includesvarious components between an engine of the vehicle and the wheels ofthe vehicle. Again, by controlling these systems, computing devices 110may also control the drivetrain of the vehicle in order to maneuver thevehicle autonomously.

Computing devices 110 of vehicle 100 may also receive or transferinformation to and from other computing devices, such as those computingdevices that are a part of the transportation service as well as othercomputing devices. FIGS. 4 and 5 are pictorial and functional diagrams,respectively, of an example system 400 that includes a plurality ofcomputing devices 410, 420, 430, 440 and a storage system 450 connectedvia a network 460. System 400 also includes vehicle 100, and vehicles100A, 100B which may be configured the same as or similarly to vehicle100. Although only a few vehicles and computing devices are depicted forsimplicity, a typical system may include significantly more.

As shown in FIG. 4 , each of computing devices 410, 420, 430, 440 mayinclude one or more processors, memory, data and instructions. Suchprocessors, memories, data and instructions may be configured similarlyto one or more processors 120, memory 130, data 134, and instructions132 of computing devices 110.

The network 460, and intervening nodes, may include variousconfigurations and protocols including short range communicationprotocols such as Bluetooth, Bluetooth LE, the Internet, World Wide Web,intranets, virtual private networks, wide area networks, local networks,private networks using communication protocols proprietary to one ormore companies, Ethernet, WiFi and HTTP, and various combinations of theforegoing. Such communication may be facilitated by any device capableof transmitting data to and from other computing devices, such as modemsand wireless interfaces.

In one example, one or more computing devices 410 may include one ormore server computing devices having a plurality of computing devices,e.g., a load balanced server farm, that exchange information withdifferent nodes of a network for the purpose of receiving, processingand transmitting the data to and from other computing devices. Forinstance, one or more computing devices 410 may include one or moreserver computing devices that are capable of communicating withcomputing devices 110 of vehicle 100 or a similar computing devices ofvehicle 100A as well as computing devices 420, 430, 440 via the network460. For example, vehicles 100, 100A, may be a part of a fleet ofvehicles that can be dispatched by server computing devices to variouslocations. In this regard, the server computing devices 410 may functionas a validation computing system which can be used to validateautonomous control software which vehicles such as vehicle 100 andvehicle 100A may use to operate in an autonomous driving mode. Inaddition, server computing devices 410 may use network 460 to transmitand present information to a user, such as user 422, 432, 442 on adisplay, such as displays 424, 434, 444 of computing devices 420, 430,440. In this regard, computing devices 420, 430, 440 may be consideredclient computing devices.

As shown in FIG. 4 , each client computing devices 420, 430, 440 may bea personal computing devices intended for use by a user 422, 432, 442,and have all of the components normally used in connection with apersonal computing devices including a one or more processors (e.g., acentral processing unit (CPU)), memory (e.g., RAM and internal harddrives) storing data and instructions, a display such as displays 424,434, 444 (e.g., a monitor having a screen, a touch-screen, a projector,a television, or other device that is operable to display information),and user input devices 426, 436, 446 (e.g., a mouse, keyboard,touchscreen or microphone). The client computing devices may alsoinclude a camera for recording video streams, speakers, a networkinterface device, and all of the components used for connecting theseelements to one another.

Although the client computing devices 420, 430, and 440 may eachcomprise a full-sized personal computing devices, they may alternativelycomprise mobile computing devices capable of wirelessly exchanging datawith a server over a network such as the Internet. By way of exampleonly, client computing devices 420 may be a mobile phone or a devicesuch as a wireless-enabled PDA, a tablet PC, a wearable computingdevices or system, or a netbook that is capable of obtaining informationvia the Internet or other networks. In another example, client computingdevices 430 may be a wearable computing system, shown as a wristwatch asshown in FIG. 4 . As an example the user may input information using asmall keyboard, a keypad, microphone, using visual signals with acamera, or a touch screen.

In some examples, client computing devices 440 may be an operationsworkstation used by an administrator or operator to review scenariooutcomes, handover times, and validation information as discussedfurther below. Although only a single operations workstation 440 isshown in FIGS. 4 and 5 , any number of such work stations may beincluded in a typical system. Moreover, although operations work stationis depicted as a desktop computer, operations works stations may includevarious types of personal computing devices such as laptops, netbooks,tablet computers, etc.

As with memory 130, storage system 450 can be of any type ofcomputerized storage capable of storing information accessible by theserver computing devices 410, such as a hard-drive, memory card, ROM,RAM, DVD, CD-ROM, write-capable, and read-only memories. In addition,storage system 450 may include a distributed storage system where datais stored on a plurality of different storage devices which may bephysically located at the same or different geographic locations.Storage system 450 may be connected to the computing devices via thenetwork 460 as shown in FIGS. 4 and 5 , and/or may be directly connectedto or incorporated into any of the computing devices 110, 410, 420, 430,440, etc.

Storage system 450 may store various types of information as describedin more detail below. This information may be retrieved or otherwiseaccessed by a server computing devices, such as one or more servercomputing devices 410, in order to perform some or all of the featuresdescribed herein.

Example Methods

In addition to the operations described above and illustrated in thefigures, various operations will now be described. It should beunderstood that the following operations do not have to be performed inthe precise order described below. Rather, various steps can be handledin a different order or simultaneously, and steps may also be added oromitted.

Computing devices 110 may maneuver vehicle 100 to a destinationlocation, for instance, to transport cargo and/or one or morepassengers. In this regard, computing devices 110 may initiate thenecessary systems to control the vehicle autonomously along a route tothe destination location. For instance, the routing system 168 may usethe map information of data 134 to determine a path or route to thedestination location that follows a set of connected rails of mapinformation 200. The computing devices 110 may then maneuver the vehicleautonomously (or in an autonomous driving mode) as described above alongthe route towards the destination.

For instance, FIG. 6 depicts vehicle 100 being maneuvered on a sectionof roadway 600 including intersections 602 and 604 corresponding to themap information 200. In this example, intersections 602 and 604correspond to intersections 202 and 204 of the map information 200,respectively. In this example, lane lines 610, 612, and 614 correspondto the shape, location, and other characteristics of lane lines 210,212, and 214, respectively. Similarly, crosswalk 630 corresponds to theshape, location, and other characteristics of crosswalk 230,respectively; sidewalks 640 correspond to sidewalks 240; traffic lightsignal lights 620, 622 correspond to traffic light signal lights 220,222, respectively; stop signs 650, 652 correspond to stop signs 250,252, respectively; and yield sign 660 corresponds to yield sign 260. Inaddition, various vehicles 690-494 are arranged at different locationsaround roadway 600.

As the vehicle 100 moves through its environment, the vehicle'sperception system 172 may provide the computing devices with sensor dataincluding information about the vehicle's environment. As noted above,this sensor data may include the sensor data may include the locations,orientations, heading, shapes, sizes, types of features of the mapinformation as well as other objects. This information may be used todetermine how to maneuver the vehicle based on a combination of thisinformation, map data, as well as rules for responding to differentsituations.

For instance, example 700 of FIG. 7 depicts features of the environmentof vehicle 100 (as in the example 600 of FIG. 6 ) with bounding boxesfor objects 790-794 representing the general shape and location ofvehicles 690-694 as provided to the computing devices 110 by perceptionsystem 172. In this example, the routing system 168 use map information200 to determine a route 670 for vehicle 100 to follow in order to reacha destination (not shown), and using this information and the rules ofmemory 130, the computing devices 110 may determine a trajectory for thevehicle to track over the next few seconds in order to follow the route.

The sensor data may be tracked over time to determine the behavior ofeach object, for instance, how the object is moving through the world.For instance, example 800 of FIG. 8 depicts features of the environmentof vehicle 100 at some point in time later than FIG. 7 , such as a fewseconds or more or less into the future. Again, the bounding boxes forobjects 790-794 represent the general shape and location of vehicles690-694 as provided to the computing devices 110 by perception system172. By comparing the sensor data for such objects over time (forinstance between FIGS. 7 and 8 and points between), the computingdevices 110 may determine the behavior of these objects.

In order to detect a procession, the sensor data from the perceptionsystem 172 and the aforementioned determined behaviors may be analyzedto determine whether any detected objects are disobeying one of therules (i.e. not following traffic precedence). Using this information, aprocession detector, for instance a software module, of the instructions132 of computing devices 110 may be used to detect whether the object isinvolved in a procession.

The detector may be run continuously, that is identifying an initialobject as disobeying a rule may be part of the processing performed bythe detector. Alternatively, the detector may be run in response todetecting one or more objects disobeying one of the rules. Theprocession detector may compare the observed behaviors to the rules inorder to determine whether an object is obeying or disobeying a rule.For instance, each of bounding boxes 790, 792 may have been observedpassing through intersection 204/604 without stopping for stop sign250/650. In addition, each of bounding boxes 792 and 794 may have beenobserved passing through intersection 202/602 without stopping for a redlight at traffic light signal light 220.

The more objects that are observed disobeying the rules, and the morerules that are observed being disobeyed, the more likely that the objector objects are part of a procession, or rather, the greater theconfidence in the determination that the object or objects are part of aprocession. For instance, once a first object is observed passingthrough intersection 204/604 without stopping for stop sign 250/650 foreach additional object observed passing through intersection 204/604without stopping for stop sign 250/650, this may make it more likelythat each of those objects are part of a procession. Similarly, once afirst object is observed passing through intersection 204/604 withoutstopping for a red light at traffic light signal light 220, for eachadditional object observed passing through intersection 204/604 withoutstopping for a red light at traffic light signal light 220, this maymake it more likely that each of those objects are part of a procession.In addition, if some or all of the objects observed passing throughintersection 204/604 without stopping for stop sign 250/650 are alsoobserved passing through intersection 204/604 without stopping for a redlight at traffic light signal light 220, this may make it more likelythat each of these objects is part of a procession.

In one instance, the detector may determine whether there is aprocession based on whether one or more thresholds is met. For instance,if a threshold minimum number of objects, such as 2 or more or less, areobserved disobeying the same rule in the say way, such as all of theseobjects are passing through intersection 202/602 without stopping for ared light at traffic light signal light 220, the detector may determinethat these objects are involved in a procession. Of course, thethreshold minimum number need not be a fixed, whole number, but mayactually be probabilistic in nature and derived from the sensor datacollected from the perception system and logged in the memory 130. Foranother instance, if any number of objects appears to be consistentlydisobeying the same rule in the same way over a threshold minimum periodof time, such as 10 seconds or more or less, the detector may determinethat the objects are involved in a procession. For this example, theactual number of object sufficient to meet this threshold may depends onthe nature of the ‘rule violation’ and other surrounding traffic. Forinstance, a vehicle that just barely runs a red light is not a strongsignal, but an emergency vehicle that runs a well-established red light(or a traffic light that has been red for a second or longer) and thencomes to a stop in the middle of an intersection may be enough of asignal on its own, and so on. For yet another instance, if a minimumnumber of objects is detected disobeying the same rule in the same wayfor some minimum period of time, this may indicate that the objects areinvolved in a procession.

In other instances, whether or there is a procession may be morenuanced. For instance, the likelihood may be a cumulative value that canincrease or decrease based on a plurality of different signals andobservations. In other words, the more vehicles observed disobeying arule, the more confidence that the rule “is not in effect” for thosevehicles. In addition, the more different rules being disobeyed by thoseobserved vehicles, the more likely that there is some different set ofrules in force because those objects are part of a procession. As anexample, each additional object observed disobeying the same rule in thesame way may increase a likelihood that the objects are involved in aprocession by some value, such as 0.1 or more or less.

This value may be fixed or variable, for instance, increasing slightlyfor each additional object that is observed disobeying the same rule inthe same way. For instance, for an initial observation, the value may be0.1, but for each additional observation, the value may increase by0.05, such that the second observation would be 0.15 (added to 0.1) andthe third observation would be 0.2 (added to 0.25). In addition oralternatively, for each additional second or more or less of time thatobjects continue to disobey the same rule in the same way may alsoincrease a likelihood that the objects are involved in a procession,such as 0.1 or more or less. In addition or alternatively, for eachadditional rule that those objects disobey in the same way may alsoincrease a likelihood that the objects are involved in a procession,such as 0.1 or more or less. Similarly, in addition or alternatively,for each additional second or more or less of time that objects continueto disobey that additional rule in the same way may also increase alikelihood that the objects are involved in a procession, such as 0.1 ormore or less. Again, value may be fixed or variable, for instance,increasing slightly for each additional object that is observeddisobeying the same rule in the same way.

Other signals detected by the perception system 172 may further increaseor the likelihood of a procession. For instance, if there is one or moreemergency vehicles (such as a police car, firetruck, ambulance, or apolice motorcycle) nearby, if a group of vehicles is being led by anemergency vehicle, if an emergency vehicle pulls into an intersectionand stop, if a pedestrian (police or otherwise) is directing traffic, orif one or more motorcycles are stopping in an intersection (even withoutemergency lights or sirens), such signals may increase the likelihood ofa procession, again by some fixed or variable amount. More complexsituations, such as “leap-frogging” emergency or emergency vehiclesdoing things that typical vehicles would otherwise not do, may also besignals which may increase the likelihood of a procession, again by somefixed or variable amount. As an example, the aforementionedleap-frogging may be typical in processions which are being accompaniedby police or other emergency vehicles which are temporarily stoppingtraffic to allow the procession to proceed. This may include a firstemergency vehicle passing another, stopping (for instance at a firstintersection to stop cross-traffic), which is by a second emergencyvehicle which thereafter stops (for instance, to stop cross-traffic at asecond intersection), and thereafter the first emergency vehiclesproceeds to pass the second emergency vehicle and thereafter stop (forinstance, to stop cross-traffic at a third intersection), and so on.

Visual signals captured, for instance, by one or more cameras of theperception system 172 may also be used to increase the likelihood of aprocession, again by some fixed or variable amount. As an example, suchvisual signals may include those that would identify an object as ahearse (using a hearse detector), flags or window badges typically usedin funeral processions and motorcades or on a lead vehicle of suchprocessions, identifying pedestrians walking in a street with or withoutmusic playing, identifying pedestrians holding flags, signs or bannersas well as the text on those, may also increase the likelihood of aprocession.

Some signals detected by the perception system 172 may actually decreasethe likelihood of a procession, again by some fixed or variable amount.If there are objects which do not appear to be disobeying the same rulein the same way, this may decrease or have no effect on the likelihoodof a procession. Similarly, if there is a gap in time during whichobjects are obeying the rule, this may decrease the likelihood of aprocession. In this case, the longer the gap, the greater the decreasein the likelihood of a procession. In some instances, local regulationsmay be taken into account by the detector. For instance, if funeralprocessions which disobey traffic lights or parades are forbidden incertain areas, this may decrease the likelihood of a procession.

Once this cumulative likelihood meets a certain threshold, the detectormay determine that there is a procession. For instance, based on theobservations of vehicles 690-694 by way of objects 790-794 and any othersignals, the likelihood of a procession may be determined to be 80%. Ifthe threshold is 75%, the detector may determine that there is aprocession.

In some instances, the detector may even determine a type of theprocession. For instance, if the procession involves pedestrians, thismay indicate a parade or march. As another instance, if the processioninvolves a plurality of passenger vehicles, this may indicate a funeralprocession or motorcade. At the same time, if the procession involves aplurality of motorcycles or pedestrians, this may indicate a parade. Asyet another instance, if the procession includes vehicles with specificcharacteristics, for instance, vehicles marked with flags or text thatindicate they are part of a funeral procession (as determined fromimages captured by a camera of the perception system), this may indicatea funeral procession.

Once the detector has determined that there is a procession, thisinformation may be sent to the vehicle's planner and used in order todetermine how to respond to the procession and therefore how to controlthe vehicle. For instance, there may be a default response to allprocessions to yield to the objects in a procession until the processionis over. In that regard, vehicle 100 may yield to all of objects 790-794as these objects pass through intersection 202/602, even though thevehicle would otherwise have had the right of way to proceed through theintersection before one or more of the objects 790-794. In addition oralternatively, rather than a default response, certain types ofbehaviors, such as proceeding through an intersection where the vehicle100 has the right of way, may no longer be an option when determininghow to control the vehicle. In addition or alternatively, for certaintypes of processions, additional actions, such as requesting assistanceor confirmation of a procession from a remote operator, such as bysending a request to operations workstation 440 via network 460, mayalso be taken.

In addition, the detector may identify the objects that are included inthe procession, or rather, all of the objects that are disobeying thesame rule in the same way. As such, these objects may be “grouped”together, or that is, responded to as a single object (even if they aretracked by the perception system separately). In this regard, if thedetermined response is to yield, the vehicle may be controlled to yieldto all of the objects as a group. For instance, if all of objects790-794, are determined to be part of a procession, the computingdevices 110 may control the vehicle 100 to yield to all of theseobjects. This may keep the vehicle becoming aggressive or assertiveinappropriately. As an example, if a funeral procession is passingthrough an intersection, the vehicle may have an opportunity to “cutinto” the line in order to make a right turn because without thedetector. This may be because the vehicle 100 may simply determine thatthe objects in the procession were merely slow moving traffic. Thus, bydetecting a procession, the vehicle 100 would not move between theobjects in a group of objects. As such this type of cut-in behavior,which is generally frowned upon and can be disconcerting to those in theprocession, may be avoided.

FIG. 9 is a flow diagram 900 that may be performed by one or moreprocessors, such as one or more processors 120 of computing devices 110in order to detect and respond to processions. For instance, at block910, sensor data identifying two or more objects in an environment of avehicle is received. At block 920, whether the two or more objects aredisobeying a predetermined rule in a same way is determined. At block930, based on the determination that the two or more objects aredisobeying a predetermined rule, whether the two or more objects areinvolved in a procession is determined. At block 940, the vehicle iscontrolled autonomously in order to respond to the procession based onthe determination that the two or more objects are involved in aprocession.

The features described above provide for a useful way to detect andrespond to procession. By doing so, the vehicle can respond to theprocession, for instance yielding appropriately. This may keep thevehicle becoming aggressive or assertive inappropriately as well aspreventing an uncomfortable stoppage of traffic by not appropriatelyresponding, for instance not yielding or cutting into, to processions.

Unless otherwise stated, the foregoing alternative examples are notmutually exclusive, but may be implemented in various combinations toachieve unique advantages. As these and other variations andcombinations of the features discussed above can be utilized withoutdeparting from the subject matter defined by the claims, the foregoingdescription of the embodiments should be taken by way of illustrationrather than by way of limitation of the subject matter defined by theclaims. In addition, the provision of the examples described herein, aswell as clauses phrased as “such as,” “including” and the like, shouldnot be interpreted as limiting the subject matter of the claims to thespecific examples; rather, the examples are intended to illustrate onlyone of many possible embodiments. Further, the same reference numbers indifferent drawings can identify the same or similar elements.

The invention claimed is:
 1. A method of controlling a vehicleautonomously, the method comprising: receiving, by one or moreprocessors, sensor data; determining, by the one or more processorsbased on the sensor data, a number of objects that are disobeying apredetermined traffic rule; and controlling, by the one or moreprocessors, the vehicle autonomously based on the determined number ofobjects that are disobeying a predetermined traffic rule.
 2. The methodof claim 1, wherein the sensor data identifies two or more objects in anenvironment of the vehicle.
 3. The method of claim 2, wherein the two ormore objects are disobeying the predetermined traffic rule in a sameway.
 4. The method of claim 3, further comprising, determining, by theone or more processors, an amount of time that the two or more objectsare disobeying the predetermined traffic rule, wherein the controllingof the vehicle is further based on the determined amount of time.
 5. Themethod of claim 2, wherein the predetermined traffic rule defines atraffic precedence that the two or more objects are disobeying.
 6. Themethod of claim 2, further comprising, determining, by the one or moreprocessors, a number of additional objects each disobeying thepredetermined traffic rule, wherein the controlling of the vehicle isfurther based on the determined number of objects.
 7. The method ofclaim 2, further comprising, determining, by the one or more processors,an amount of time during which additional objects also disobey thepredetermined traffic rule, wherein the controlling of the vehicle isfurther based on the determined amount of time.
 8. The method of claim1, further comprising, determining, by the one or more processors, athreshold minimum number of objects determined to be disobeying thepredetermined traffic rule, wherein the controlling of the vehicle isfurther based on the threshold minimum number.
 9. The method of claim 8,wherein the threshold minimum number of objects disobey thepredetermined traffic rule over at least a threshold minimum period oftime.
 10. The method of claim 1, further comprising, determining, by theone or more processors, objects determined to be disobeying thepredetermined traffic rule over at least a threshold minimum period oftime, wherein the controlling of the vehicle is further based on theobjects determined to be disobeying the predetermined traffic rule overat least the threshold minimum period of time.
 11. The method of claim1, wherein the predetermined traffic rule includes one of stopping for ared traffic light signal or stopping for a stop sign.
 12. A method ofcontrolling a vehicle autonomously, the method comprising: receiving, byone or more processors, sensor data; determining, by the one or moreprocessors based on the sensor data, a number of objects that aredisobeying a predetermined traffic rule; determining, by the one or moreprocessors based on the sensor data, an amount of time during which thedetermined number of objects are disobeying a predetermined trafficrule; and controlling, by the one or more processors, the vehicleautonomously based on the determined amount of time during which thedetermined number of objects are disobeying a predetermined trafficrule.
 13. The method of claim 12, wherein the sensor data identifies twoor more objects in an environment of the vehicle.
 14. The method ofclaim 13, wherein the two or more objects are disobeying thepredetermined traffic rule in a same way.
 15. The method of claim 14,wherein the predetermined traffic rule defines a traffic precedence thatthe two or more objects are disobeying.
 16. An autonomous vehicle,comprising: at least one sensor; and one or more processors configuredto: receive sensor data from the at least one sensor; determine, basedon the sensor data, a number of objects that are disobeying apredetermined traffic rule; and control the autonomous vehicle based onthe determined number of objects that are disobeying a predeterminedtraffic rule.
 17. The autonomous vehicle of claim 16, wherein the sensordata identifies two or more objects in an environment of the vehicle.18. The autonomous vehicle of claim 17, wherein the two or more objectsare disobeying the predetermined traffic rule in a same way.
 19. Theautonomous vehicle of claim 18, wherein the one or more processors arefurther configured to determine an amount of time that the two or moreobjects are disobeying the predetermined traffic rule, and control thevehicle autonomously based on the determined amount of time.
 20. Theautonomous vehicle of claim 17, wherein the predetermined traffic ruledefines a traffic precedence that the two or more objects aredisobeying.
 21. The method of claim 1, further comprising: estimating,by the one or more processors, a likelihood that the objects areinvolved in a procession based on the determined number of objects,wherein the likelihood increases as the determined number of objectsincreases, and the vehicle is further controlled autonomously based onthe estimated likelihood.
 22. The method of claim 12, furthercomprising: estimating, by the one or more processors, a likelihood thatthe objects are involved in a procession based on the determined numberof objects, wherein the likelihood increases as the determined number ofobjects increases, and the vehicle is further controlled autonomouslybased on the estimated likelihood.
 23. The autonomous vehicle of claim16, wherein the one or more processors are configured to estimate alikelihood that the objects are involved in a procession based on thedetermined number of objects, wherein the likelihood increases as thedetermined number of objects increases, and the autonomous vehicle isfurther controlled based on the estimated likelihood.